System and Method for Dynamic Content Delivery Based on Gaze Analytics

ABSTRACT

A method of presenting content to a subject based on eye position measurements is provided. The method includes presenting the subject with content. While presenting the content to the subject, one or more of the subject&#39;s eye positions is measured. The method further includes continuously performing the operations of generating a variability metric using the one or more measured eye positions and comparing the variability metric with a predetermined baseline to determine an attention state of the subject. Upon detection of a change in the attention state, the presentation of the content is modified.

RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 61/898,430, filed Oct. 31, 2013, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

The disclosed embodiments relate generally to systems and methods ofdelivering content, such as multimedia content. More specifically, thedisclosed embodiments relate to methods and systems for dynamic contentdelivery based on eye tracking (e.g., gaze) analytics.

BACKGROUND

Techniques to monitor and/or track a person's eye movements (forexample, to detect a locus of the person's gaze) have been used in avariety of contexts. However, as described in more detail below,determining a locus of a person's gaze, however, does not provideinsight into the quality of that person's level of attention, andtherefore decisions based solely on the locus of a person's gaze mayresult in unsatisfactory man-machine interactions.

SUMMARY

Accordingly, there is a need for devices that are able to determine auser's level of attention and interact with the user by providingcontent dynamically in accordance with the user's level of attention. Inaccordance with some embodiments, a method, system, andcomputer-readable storage medium are proposed for dynamic contentdelivery based on gaze (e.g., eye tracking) analytics. For example, insome embodiments, content is dynamically provided in accordance with theuser's level of attention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system for delivering content based on gazeanalytics, in accordance with some embodiments.

FIG. 2 is a block diagram illustrating a system for dynamic contentdelivery, in accordance with some embodiments.

FIGS. 3A-3C illustrate an example of dynamic content delivery based ongaze analytics, in accordance with some embodiments.

FIGS. 4A-4C illustrate another example of dynamic content delivery basedon gaze analytics, in accordance with some embodiments.

FIGS. 5A-5D illustrate a flow diagram of a method for delivering contentdynamically based on gaze analytics, in accordance with someembodiments.

FIGS. 6A-6C illustrate a flow diagram of a method for generating acost-per-action metric for advertising, in accordance with someembodiments.

FIGS. 7A-7B illustrate a flow diagram of a method for delivering contentdynamically based on gaze analytics, in accordance with someembodiments.

Like reference numerals refer to corresponding parts throughout thedrawings.

DETAILED DESCRIPTION OF EMBODIMENTS

A portable electronic device such as a smart phone may track a user'seye movements in order to determine whether the user is looking at thescreen. When the device detects that the user is not looking at thescreen, the device may, for example, automatically pause video contentthat is being displayed on the device. However, determining a locus of aperson's gaze, does not provide insight into the quality of thatperson's level of attention. Indeed, a device may be “tricked” intocontinuing to provide video content simply because the user's eyes aredirected towards the screen, despite the fact that the user is, forexample, thinking about other things, or focused on aural stimuli (e.g.,another person talking) and is therefore not attending to the visualcontent displayed on the screen. As taught and described in more detailbelow, a determination of a user's level of attention, in addition tothe direction of their gaze, can be used to improve dynamic contentdelivery. Such information would improve user experience via a deeperlevel of user interface interaction with their device, and would allow,for example, better targeted advertising campaigns, as well as newmodels for advertisement billing.

Described in more detail below are devices that determine a user's levelof attention and interact with the user by providing content dynamicallyin accordance with the user's level of attention. To this end, inaccordance with some implementations, a method of presenting content toa subject based on eye position measurements is provided. The methodincludes presenting the subject with content. While presenting thecontent to the subject, one or more of the subject's eye positions ismeasured. The method further includes continuously performing theoperations of generating a variability metric using the one or moremeasured eye positions and comparing the variability metric with apredetermined baseline to determine an attention state of the subject.Upon detection of a change in the attention state, the presentation ofthe content is modified.

In another aspect of the present disclosure, some implementationsprovide a method of generating a cost-per-action metric for advertising.The method includes presenting a subject with an advertisement. Whilepresenting the advertisement to the subject, one or more of thesubject's eye positions is measured. The method further includesgenerating a variability metric using the one or more measured eyepositions and comparing the variability metric with a predeterminedbaseline to determine an attention state of the subject. Thecost-per-action metric is generated in accordance with the attentionstate of the subject.

In yet another aspect of the present disclosure, some implementationsprovide a method of presenting content to a subject based on eyeposition measurements. The method includes presenting the subject withfirst content. While presenting the first content to the subject, one ormore of the subject's eye positions is measured. The method furtherincludes continuously generating a variability metric using the one ormore measured eye positions and comparing the variability metric with apredetermined baseline to determine an attention state of the subject.The method further includes detecting a change in the attention state ofthe subject. In accordance with the detected change in the attentionstate of the subject, the subject is presented with second content.

In another aspect of the present invention, to address theaforementioned limitations of conventional eye tracking techniques, someimplementations provide a non-transitory computer readable storagemedium storing one or more programs. The one or more programs compriseinstructions, which when executed by an electronic device with one ormore processors and memory, cause the electronic device to perform anyof the methods provided herein.

In yet another aspect of the present invention, to address theaforementioned limitations of conventional eye tracking techniques, someimplementations provide an electronic device. The electronic deviceincludes one or more processors, memory, and one or more programs. Theone or more programs are stored in memory and configured to be executedby the one or more processors. The one or more programs include anoperating system and instructions that when executed by the one or moreprocessors cause the electronic device to perform any of the methodsprovided herein.

In accordance with some embodiments, a method, system, andcomputer-readable storage medium are provided for dynamic contentdelivery based on gaze analytics (e.g., analytics performed based on eyetracking measurements). Rather than delivering content based solely on alocus of a subject's gaze, the system, method, and computer-readablestorage medium in these embodiments calculate a variability metric basedon one or more eye position measurements. The variability metric is usedto determine the extent to which the subject is attending, for example,by using a numerical value of the variability metric or comparing thenumerical value with a baseline value in order to categorize thesubject's attention level. For example, in some embodiments, thenumerical value of the variability metric is compared to a baselinevalue associated with a demographic to which the subject belongs (suchas an age group of the subject). Alternatively, the numerical value ofthe variability metric is compared to a baseline value obtained throughone or more previous tests of the subject (sometimes called a personalbaseline, or personal baseline variability metric). In someimplementations, an attention state of the subject is categorized basedon the comparison, for example, to determine that the subject'sattention state is an attending state (e.g., the subject is payingattention), a not-attending state (e.g., the subject is not payingattention), or an about-to-attend state (e.g., the subject is trendingtowards paying attention). In some implementations, content is thendelivered in accordance with the attention state. For example, thecontent is delivered statically when the subject is not attending, andthe content is delivered at a predefined rate (e.g., a constant rate, ora rate proportional to the numerical value of the variability metric)when the subject is attending.

In accordance with some embodiments, a disconjugacy metric is describedherein which can be used as the variability metric.

In some implementations, content is delivered further in accordanceadditional determinations, such as a locus of the subject's attention(e.g., a locus of the subject's gaze). Consider an example of apre-recorded lecture delivered on a personal computing device (e.g., atablet, smart-phone, laptop computer, personal computer,smart-television, or the like). The lecture may be a multimedia lecturethat includes various types of content such as video content that isdisplayed on a region of a display and audio content that is produced byan audio speaker. In some implementations, the device will pause thelecture if the subject is not paying attention to the content (e.g., theattention state is the not-attending state or the locus of attentiondoes not correspond to the region of the display). In someimplementations, when the subject is paying attention to the content(e.g., the attention state is the attending state and the locus ofattention is the region of display) the device will speed up or slowdown delivery of the lecture based on the numerical value of theattending metric.

Reference will now be made in detail to various implementations,examples of which are illustrated in the accompanying drawings. In thefollowing detailed description, numerous specific details are set forthin order to provide a thorough understanding of the present disclosureand the described implementations herein. However, implementationsdescribed herein may be practiced without these specific details. Inother instances, well-known methods, procedures, components, andmechanical apparatus have not been described in detail so as not tounnecessarily obscure aspects of the implementations.

FIG. 1 illustrates a system 100 for delivering content based on gazeanalytics, in accordance with some embodiments. In this example, thecontent is delivered on a display 106 of an electronic device (e.g., atablet, smart-phone, laptop computer, personal computer,smart-television, or the like). The content includes visually displayedcontent (e.g., video) displayed in a region 103 of display 106. One ormore digital video cameras 104 are focused on subject 102's eyes so thateye positions (and, in some embodiments, eye movements) of subject 102are recorded. In accordance with some embodiments, digital video cameras104 are mounted on subject 102's head by head equipment 108 (e.g., aheadband, or a pair of eyeglasses). Various mechanisms are, optionally,used to stabilize subject 102's head, for instance to keep the distancebetween subject 102 and display 106 fixed, and to also keep theorientation of subject 102's head fixed as well. In one embodiment, thedistance between subject 102 and display 106 is kept fixed atapproximately 40 cm. In some implementations, head equipment 108includes the head equipment and apparatuses described in U.S. PatentPublication 2010/0204628 A1, which is incorporated by reference in itsentirety.

In some embodiments, the one or more digital video cameras 104 areincorporated into the electronic device. For example, in someembodiments, the device is a tablet computer that includes display 106and digital video cameras 104 incorporated into a single housing of thedevice. In some implementations, digital video cameras 104 are also usedfor other device tasks, such as recording multimedia message service(MMS) messages, taking photographs to send to friends and family, etc.

Display 106 is, optionally, a computer monitor, projector screen, orother display device. Display 106 and digital video cameras 104 arecoupled to computer control system 110. In some embodiments, computercontrol system 110 controls the patterns displayed and also receives andanalyzes the eye position information received from the digital videocameras 104. In some implementations, computer control system 110 isoptionally coupled with, and controls, one or more audio speakers 112.The one or more audio speakers 112, together with the computer controlsystem 110, enable the system 100 to deliver audio content in accordancewith gaze analytics.

FIG. 2 is a block diagram of a system 200 for delivering content basedon gaze analytics, in accordance with some embodiments. In someembodiments, system 200 shares one or more components with system 100described with reference to FIG. 1 (e.g., digital video camera(s) 104,display 106, and audio speaker(s) 112). While certain specific featuresare illustrated, those skilled in the art will appreciate from thepresent disclosure that various other features have not been illustratedfor the sake of brevity and so as not to obscure more pertinent aspectsof the implementations disclosed herein.

To that end, the system includes one or more processor(s) 202 (e.g.,CPUs), user interface 204, memory 212, and one or more communicationbuses 214 for interconnecting these components. In some embodiments, thesystem includes one or more network or other communications interfaces210, such as a network interface for conveying requests for content orreceiving content from a content server (e.g., a web server) and/or acontent delivery network. The user interface 204 includes one or moredigital video cameras 106, and, in some embodiments, also includesdisplay 106, a keyboard/mouse 206, and one or more feedback devices 208.In some implementations, display 106 is a touch-screen display,obviating the need for a keyboard/mouse 206.

The communication buses 214 may include circuitry (sometimes called achipset) that interconnects and controls communications between systemcomponents. Memory 212 includes high-speed random access memory, such asDRAM, SRAM, DDR RAM or other random access solid state memory devices;and may include non-volatile memory, such as one or more magnetic diskstorage devices, optical disk storage devices, flash memory devices, orother non-volatile solid state storage devices. Memory 212 mayoptionally include one or more storage devices remotely located from theprocessor(s) 202. Memory 212, including the non-volatile and volatilememory device(s) within memory 212, comprises a non-transitory computerreadable storage medium.

In some implementations, memory 212 or the non-transitory computerreadable storage medium of memory 212 stores the following programs,modules and data structures, or a subset thereof, including an operatingsystem 215, a network communication module 216, and an applicationmodule 218.

The operating system 215 includes procedures for handling various basicsystem services and for performing hardware dependent tasks.

The network communication module 216 facilitates communication withother devices via the one or more communication network interfaces 210(wired or wireless) and one or more communication networks, such as theInternet, other wide area networks, local area networks, metropolitanarea networks, and so on.

In some embodiments, application module 218 includes audio controlmodule 220, display control module 222, digital camera control module224, measurement analysis module 226, and, optionally, feedback module228. Audio control module 220 controls audio speaker(s) 112 in order todeliver audio content. Display control module 222 controls display 106in order to provided displayed content, such as video content. Digitalcamera control module 224 receives signals from digital video camera(s)104 and, where appropriate, analyzes raw data in the digital videocamera signals so as to extract values indicative of one or more of thesubject's (e.g., subject 102 in FIG. 1) eye-positions. The analysisoptionally includes facial feature recognition analysis thatdistinguishes between the subject's eyes and, for example, the subject'snose. Digital camera control module 224 also sends control signals todigital video camera(s) 104, for example, to re-focus digital videocamera(s) 104 on the subject's eye-position. Measurement analysis module226 analyzes the sensor signals to produce measurements and analyses, asdiscussed elsewhere in this document. Feedback module 228, if included,generates feedback signals for presentation to the subject via display104 or feedback devices 208. For example, display 104 may optionallypresent an indication of an attention state of the subject, such as an“attention meter” displayed on the display or a “traffic light” withred, yellow, and green lights, where red indicates that the subject isnot attending, yellow indicates that the subject is barely attending,and green indicates that the subject is attending. As another example,feedback device 208 can optionally include a tactile output devicecontrolled by feedback control module 228 which, for example, produces avibration on the subject's finger when the subject's attention starts todrift (e.g., when the user's attention state changes from the attendingstate to the not-attending state).

In some embodiments, application module 218 also stores subject data230, which includes measurement data for a subject, analysis results 234and the like. Subject measurement data can be used to generate apersonal baseline against which a variability metric of the subject iscompared (e.g., to determine an attention state of the subject). In someembodiments, application module 218 stores normative data 232, whichincludes measurement data from one or more control groups of subjects,and optionally includes analysis results 234, and the like, based on themeasurement data from the one or more control groups. In someembodiment, this control group measurement data can be used to generatea baseline value against which a variability metric of the subject iscompared (e.g., to determine an attention state of the subject). In someembodiments, the control groups include one or more control groupsubjects that match a demographic of the subject (e.g., an age range,gender, socio-economic status, etc.)

Still referring to FIG. 2, in some embodiments, digital video camera(s)104 include one or more digital video cameras focused on the subject'spupil. In some embodiments, digital video camera(s) 104 operate at apicture update rate of at least 200 hertz. In some embodiments, the oneor more digital video cameras are infrared cameras, while in otherembodiments, the cameras operate in other portions of theelectromagnetic spectrum. In some embodiments, the resulting videosignal is analyzed by processor(s) 202, under the control of measurementanalysis module 226, to determine the screen position(s) where thesubject focused, and the timing of when the subject focused at one ormore predefined screen positions.

In some embodiments, not shown, the system shown in FIG. 2 is dividedinto two systems, one which measures a subject and collects data, andanother which receives the collected data, analyzes the data anddelivers content in accordance with the collected and analyzed data(e.g., a server system).

Eye Position Calibration and Measurements.

In some embodiments, in order to provide accurate and meaningful realtime measurements of where the user is looking at any one point in time,eye position measurements (e.g., produced via digital video cameras 104)are calibrated by having the subject focus on a number of points on adisplay (e.g., display 106) during a calibration phase or process. Forinstance, in some embodiments, the calibration is based on nine pointsdisplayed on the display, include a center point, positioned at thecenter of the display and eight points along the periphery of thedisplay. In some embodiments, the eight points correspond to locationshaving angular positions at 45 degree increments with respect to thecenter. The subject is asked to focus on each of the calibration points,in sequence, while digital video cameras (e.g., digital video cameras104) measure the pupil and/or eye position of the subject. Optionally,dynamic content is displayed at each point during the calibration tohelp ensure that the user focuses on that point. The resultingmeasurements are then used by a computer control system (e.g., computercontrol system 110) to produce a mapping of eye position to screenlocation, so that the system can determine the position of the displayat which the user is looking at any point in time. In other embodiments,the number of points used for calibration may be more or less than ninepoints, and the positions of the calibration points may distributed onthe display in various ways.

The resulting measurements can also be used to produce a personalbaseline variability metric for future comparison. For example, futuremeasurements can be used to generate a variability metric, and thevariability metric can be compared to the personal baseline variabilitymetric to determine an attention level of the subject.

In some implementations, a calibration process (e.g., a calibrationprocess less extensive than an initial calibration process) is performedeach time a subject utilizes a dynamic content delivery feature of thesystem, because small differences in head position relative to thecameras, and small differences in position relative to the display 106,can have a large impact on the measurements of eye position, which inturn can have a large impact on the “measurement” or determination ofthe display position at which the subject is looking. The calibrationprocess can also be used to verify that the subject (e.g., subject 102)has a sufficient range of oculomotor movement to make use of thefeature.

In some embodiments, the eye position measurements are used to determineeye movement reaction times and the variability metric is based on theeye movement reaction times. In some embodiments, the eye movementreaction times are measured by a digital video infrared camera (e.g.,digital video camera 104) focused on the subject's pupil, operating at apicture update rate of at least 200 hertz. The resulting digital videosignal is analyzed by a computer to determine the screen position(s)where the subject was focusing, and the timing of when the subjectfocused at the appropriate screen position.

In some embodiments, the calibration process is performed by asking thesubject to follow a moving object for eight to twenty clockwise circularorbits. For example, in some embodiments, the subject is asked to followthe moving object for twelve clockwise circular orbits having a rate ofmovement of 0.4 Hz, measured in terms of revolutions per second.Furthermore, in some embodiments, the subject is asked to follow themoving object for two or three sets of eight to twenty clockwisecircular orbits, with a rest period between. The angular amplitude ofthe moving object, as measured from the subject's eyes, is about 5degrees in the horizontal and vertical directions. In other embodiments,the angular amplitude of the moving object is in the range of 3 to 10degrees.

The eye positions of the subject can be divided into horizontal andvertical components for analysis. Thus, in some embodiments, four setsof measurements are made of the subject's eye positions: left eyehorizontal position, left eye vertical position, right eye horizontalposition, and right eye vertical position. In some embodiments, one ortwo sets of two dimensional measurements (based on the movement of oneor two eyes of the subject) are used for analysis of the subject. Insome embodiments, the sets of measurements are used to generate avariability metric. In some embodiments, the variability metric is adisconjugacy metric generated by using a binocular coordinationanalysis, as described in greater detail below.

In some embodiments, the video cameras (e.g., digital video cameras 104)take pictures of the subject's eye or eyes at least 400 times per second(i.e., the video cameras having a picture frame rate of at least 400frames per second). For instance, in some embodiments, the video camerastake pictures of the subject's eye or eyes about 500 times per second,or about once every 2 milliseconds. In some embodiments, the variabilitymetric is based on a data from a shifting window of time characterizedby a duration of at least one second. Thus, the video cameras take atleast 400 pictures of each eye, thereby providing a significant amountof data on which to base an update to the variability metric.

Furthermore, as discussed in more detail in published U.S. PatentPublication 2006/0270945 A1, and 2008/0206727 A1, which are incorporatedby reference in their entirety, measurements of a subject's point offocus (sometimes referred to herein as a locus of attention) whileattempting to smoothly pursue a moving displayed object can also beanalyzed so as to provide one or more additional metrics, such asvarious tracking metrics, attention metrics, accuracy metrics,variability metrics, and so on.

Disconjugacy of Binocular Coordination.

Many people have one dominate eye (e.g., the right eye) and onesubservient eye (e.g., the left eye). For these people, the subservienteye follows the dominate eye as the dominate eye tracks an object. Insome embodiments, a disconjugacy metric is calculated to measure howmuch the subservient eye lags behind the dominate eye while the dominateeye is tracking an object. Impairment due to loss of attention, fatigue,distraction, sleep deprivation, aging, alcohol, drugs, hypoxia,infection, clinical neurological conditions (e.g., ADHD, schizophrenia,and autism), and/or brain trauma (e.g., head injury or concussion) canincrease the lag (e.g., in position or time) or differential (e.g., inposition or time) between dominate eye movements and subservient eyemovements, and/or increase the variability of the lag or differential,and thereby increase the corresponding disconjugacy metric.

In some embodiments, the disconjugacy of binocular coordination is thedifference between the left eye position and the right eye position at agiven time, and is calculated as:

Disconj(t)=POS_(LE)(t)−POS_(RE)(t)

where “t” is the time and “POS_(LE) (t)” is the position of thesubject's left eye at time t and “POS_(RE)(t)” is the position of thesubject's right eye at time t. In various embodiments, the disconjugacymeasurements include one or more of: the difference between the left eyeposition and the right eye position in the vertical direction (e.g.,POS_(RE) _(x) (t) and POS_(LE) _(x) (t)); the difference between theleft eye position and the right eye position in the horizontal direction(e.g., POS_(RE) _(y) (t) and POS_(LE) _(y) (t)); the difference betweenthe left eye position and the right eye position in the two-dimensionalhorizontal-vertical plane (e.g., POS_(RE) _(xy) (t) and POS_(LE) _(xy)(t)); and a combination of the aforementioned.

In some embodiments, a variability metric is based on disconjugacyduring shifting window of time (e.g., 0.5 seconds, 1 second, 2 seconds,etc). To quantify the disconjugacy during a respective window of time,the standard deviation of disconjugate eye positions (SDDisconj) duringthe respective window of time is calculated. In some embodiments, thevariability metric is generated in accordance with the standarddeviation of the disconjugate eye positions during the respective windowof time. In accordance with some embodiments, SDDisconj for a set of “N”values is calculated as:

${SDDisconj}_{N} = \sqrt{\frac{1}{N}{\sum\limits_{i = 1}^{N}\left( {x_{i} - {\langle x\rangle}} \right)^{2}}}$

where “x” is a disconjugate measurement discussed above (e.g.,Disconj(t)) and “<x>” represents the average value of the disconjugateeye positions. Thus, in various embodiments, SD Disconj_(N) represents:the standard deviation of disconjugate eye positions in the verticaldirection; the standard deviation of disconjugate eye positions in thehorizontal direction; or the standard deviation of disconjugate eyepositions in the two-dimensional horizontal-vertical plane. In someembodiments, a separate SDDisconj measurement is calculated for two ormore of the vertical direction, the horizontal direction, and thetwo-dimensional horizontal-vertical plane.

Therefore, in various embodiments, disconjugacy measurements, standarddeviation of disconjugacy measurements and one or more relatedmeasurements (e.g., a variability of eye position error measurement, avariability of eye velocity gain measurement, an eye position errormeasurement, and/or a rate or number of saccades measurement) arecalculated. Furthermore, in various embodiments, the disconjugacymeasurements, standard deviation of disconjugacy measurements, andrelated measurements are calculated for one or more of: the verticaldirection; the horizontal direction; the two-dimensionalhorizontal-vertical plane; and a combination of the aforementioned.

In some embodiments, one or more of the above identified measurementsare obtained for a subject and then compared with normative data (e.g.,derived measurements for other individuals). In some embodiments, one ormore of the above identified measurements are obtained for a subject andthen compared with the derived measurements for the same subject at anearlier time. For example, changes in one or more derived measurementsfor a particular person are used to evaluate improvements ordeterioration in the person's attention. Distraction, fatigue, ordisinterest are often responsible for deterioration in the person'sability to pay attention to content that is provided to them. In someembodiments, decreased attention, caused by fatigue or a distractor, canbe measured by comparing changes in one or more derived measurements fora particular person. In some embodiments, decreased attention can bemeasured by monitoring error and variability during smooth eye pursuit.

Examples and Methods of Dynamic Content Delivery.

FIGS. 3A-3C illustrate an example of dynamic content delivery based ongaze analytics, in accordance with some embodiments. For ease ofexplanation, the example illustrated in FIGS. 3A-3C is described withreference to visually displayed content. It should be understood,however, that in some embodiments, the content may also include audiocontent, tactile content, or a combination thereof.

FIG. 3A illustrates visual content displayed on display 106. A portionof Lincoln's Gettysburg Address is displayed, for example, by a webbrowser. A car advertisement is displayed in a region 302 of thedisplay. Also shown in FIG. 3A is an attention indicator 304 whichindicates a locus of attention 304-a. The attention indicator 304 is nottypically displayed on display 106 (although it can be), but is ratherprovided in FIGS. 3A-3C for ease of explanation of FIGS. 3A-3C. In thisexample, the dashed circle of attention indicator 304 in FIG. 3Aindicates that the subject's attention state is a not-attending state.Determination of the subject's attention state is described below withreference to methods 500, 600, and 700. FIG. 3B illustrates that thesubject's attention has shifted to a locus of attention 304-b, which isnow over region 302. In addition, the solid circle of attentionindicator 304 indicates that the subject's attention state is anattending state.

FIG. 3C illustrates modification of the advertisement in accordance withthe system's determination that the locus of attention is the region 302(FIG. 3B) and that the subject's attention state is the attending state.In particular, in this example the system modifies the advertisement byexpanding the advertisement to a larger region 306. In additional, forexample, the advertisement may transition to footage of the car beingdriven by a professional driver on a closed course. In addition, in someembodiments, a cost-per-action metric is generated following themodification. This provides a cost-basis for which the advertiser can becharged appropriately for the impact of their advertisement, as well asa manner in which to improve targeted advertising campaigns.

FIGS. 4A-4C illustrate another example of dynamic content delivery basedon gaze analytics, in accordance with some embodiments. For ease ofexplanation, the example illustrated in FIGS. 4A-4C is described withreference to visually displayed content. It should be understood,however, that in some embodiments, the content may also include audiocontent, tactile content, or a combination thereof.

FIG. 4A illustrates visual content displayed on display 106. For thepurposes of this example, display 106 is assumed to be a display of asmart-television. A sporting event (e.g., an archery event) is displayedas a live broadcast, for example, on the smart-television. Also shown inFIG. 4A is an attention indicator 402 which indicates a locus ofattention 402-a. In addition, the solid circle of attention indicator402 in FIGS. 4A-4C indicates that the subject's attention state is theattending state. A ticker 404 that provides scrolling scores from othersporting events is also displayed on display 106. In some circumstances,the ticker 404 alternatively displays new headlines, stock prices,elections results, etc.

FIG. 4B illustrates that the subject's attention has shifted to a locusof attention 402-b, which is now over ticker 404, and the subject'sattention state is the attending state. In some embodiments, when thelocus of attention 402-b is over the ticker 404 and the subject'sattention state is the attending state, the ticker pauses, allowing thereader to read the content that is displayed. In addition, FIG. 4Billustrates movement of the locus of attention from 402-b to 402-c (asshown in FIG. 4C).

FIG. 4C illustrate modification of the content of ticker 404 inaccordance with the system's determination that subject is attending andmoving his or her locus of attention from 402-b (FIG. 4B) to 402- c. Inparticular, the system modifies the content of the ticker 404 by“dragging” the content back (e.g., reversing the scrolling of the ticker404) in accordance with the locus of attention, thus allowing thesubject to read content that he or she may have missed.

FIGS. 5A-5D illustrate a flow diagram of a method 500 for deliveringcontent dynamically based on gaze analytics, in accordance with someembodiments. Method 500 is, optionally, governed by instructions thatare stored in a computer memory or non-transitory computer readablestorage medium (e.g., memory 212 in FIG. 2) and that are executed by oneor more processors (e.g., processor(s) 202) of one or more computersystems, including, but not limited to, system 100 (FIG. 1) and/orsystem 200 (FIG. 2). The computer readable storage medium may include amagnetic or optical disk storage device, solid state storage devicessuch as Flash memory, or other non-volatile memory device or devices.The computer readable instructions stored on the computer readablestorage medium may include one or more of: source code, assemblylanguage code, object code, or other instruction format that isinterpreted by one or more processors. In various implementations, someoperations of the method may be combined and/or the order of someoperations may be changed from the order shown in the figures. Also, insome implementations, operations shown in separate figures and/ordiscussed in association with separate methods (e.g., method 600, FIG. 6or method 700, FIG. 7) may be combined to form other methods, andoperations shown in the same figure and/or discussed in association withthe same method may be separated into different methods. Moreover, insome implementations, one or more operations in the methods areperformed by modules of system 200 shown in FIG. 2, including, forexample, processor(s) 202, user interface 204, memory 212, networkinterface 210, and/or any sub modules thereof.

In some implementations, method 500 is performed at a system includingone or more processors and memory storing instructions for execution bythe one or more processors (e.g., system 100 or system 200). Method 500includes presenting (502) a subject with content. In some embodiments,the content includes (504) visual content displayed on a display. Insome embodiments, the content includes (506) a plurality of sub-contenttokens. For example, in some circumstances, the content is multimediacontent and the sub-content tokens include video and audio frames. Insome circumstances, the content includes text content that is presentedeither visually or aurally, and the sub-content tokens include words orgroups of words (e.g., the words or groups of words are displayed, or,alternatively, rendered as speech using a text-to-speech engine).

While presenting the content to the subject, the system measures (508)one or more of the subject's eye positions. In some embodiments,measuring the one or more of the subject's eye positions includes (510)measuring the subject's right eye positions and measuring the subject'sleft eye positions. In some embodiments, measuring the subject's eyepositions is accomplished (512) by using one or more video cameras. Inaccordance with these embodiments, FIG. 1 shows a system includingdigital video camera(s) 104 for measuring subject 102's eye positions.

The system continuously generates (514) a variability metric using theone or more measured eye positions. In this context, the term“continuously,” means periodically generating new values of a respectivemetric at a rate of at least 5 Hz (i.e., five new values per second). Insome embodiments, generating the variability metric using the one ormore measured eye positions includes (516) comparing the measured righteye positions with the measured left eye positions. In some embodiments,generating the disconjugacy metric includes calculating a plurality ofresults of the difference of the position of the subject's right eyefrom the position of the subject's left eye (e.g., by subtracting theposition of the subject's right eye from the position of the subject'sleft eye). In some embodiments, generating the disconjugacy metricincludes calculating a variability (e.g., a dispersion, scatter, orspread) of the plurality of results, where each result corresponds to adistinct time. For example, in some embodiments, the disconjugacy metriccorresponds (518) to a standard deviation of differences between thesubject's right eye position and the subject's left eye position over ashifting window of time. In some embodiments, generating thedisconjugacy metric includes generating a vertical metric and generatinga horizontal metric, where generating a vertical metric includesmeasuring the difference between the subject's eyes along a verticalaxis and generating a horizontal metric includes measuring thedifference between the subject's eyes along a horizontal axis.

In some embodiments, the system compares the measured eye movements witha movement of a focus of the content. For example, the content mayinclude a television program. In these circumstances, it is expectedthat a subject who is attending to the television program will trackwhichever actor in the television program is speaking Thus, in somecircumstances, the system will compare the measured eye movements to themovements of the speaking actors on the screen. The variability metriccorresponds to how accurately and how consistently the subject visuallytracks movement of the focus of content. To this end, in someembodiments, the system measures both of the subject's eye movements.Alternatively, in some embodiments, the system measures the eyemovements of one of the subject's eyes (e.g., the subject's dominateeye).

In some embodiments, the variability metric (regardless of whether it isa disconjugacy metric or not) is generated (520) based on at least oneof: a variability of eye position error metric, a variability of eyevelocity gain metric, an eye position error metric, and a rate or numberof saccades metric.

In any event, the system continuously compares (522) the variabilitymetric with a predetermined baseline to determine an attention state ofthe subject. In some embodiments, the attention state is (524) one of anabout-to-attend state, an attending state, or a not-attending state. Forexample, in some embodiments, the attending state corresponds to a lowvariability metric and indicates that the subject is fully attending tothe presented content. In some implementations, the subject's attentionstate is the about-to-attend state when the variability metric isdecreasing—indicating an increasing level of attention—and thevariability metric is greater than the predetermined baseline but withina predetermined threshold of the predetermined baseline. In someimplementations, the attention state is the not-attending state when theattention state is not the attending state. Alternatively, the attentionis state is the not-attending state when the attention state is not theattending state or the about-to-attend state.

In some embodiments, the predetermined baseline is (526) based on atleast one of: a variability range (e.g., a disconjugacy range)associated with a preselected group of control subjects (sometimescalled a control group baseline), a demographic of the subject (e.g., anage range, a gender, a socio-economic status, etc.) and a variabilitymetric (e.g., a disconjugacy metric) for the subject generated from aprevious test (sometimes called an individual or personal baseline). Foran example, an individual or personal baseline may be obtained by askingthe individual to track a smoothly moving object on the display for apredefined period of time (e.g., 5 seconds, or 10 seconds), and usingeye measurements obtained during the predefined period of time togenerate the personal baseline (e.g., the predefined period of timecomprises a personal baseline calibration period).

In some embodiments, the group of control subjects is composed ofpersons sharing a demographic of the subject. For example, the group ofcontrol subjects is composed of persons having a similar age andsocioeconomic status as the subject. In some embodiments, the group ofcontrol subjects is composed of persons having a similar braindevelopment level as the subject. In some embodiments, the group ofcontrol subjects is composed of persons of the same gender as thesubject. In some accordance with some implementations, an increase inthe variability metric over time may be indicative of fatigue, impairedmental state induced by drugs/alcohol, or waning attention level.

In some embodiments (e.g., when the content includes displayed visualcontent), the system continuously determines (528) a locus of attentionon the display using the one or more measured eye positions. In someimplementations, using the continuously determined locus of attention,the system determines (530) a trajectory of the locus of attention.

Upon detection of a change in the attention state, the system modifies(532) the presentation of the content. In some implementations,presentation of the content is modified (534) in accordance with boththe change in attention state (532) and the continuously generated locusof attention. In some implementations, presentation of the content ismodified (536) in accordance with both the change in attention state(532) and the trajectory of the locus of attention.

In some implementations (e.g., when the content includes a plurality ofsub-content tokens), in accordance with the detection of the change inattention state (532) and a determination that the attention state isthe not-attending state, the system statically presents (538) a firstsub-content token. On the other hand, in accordance with the detectionof the change in attention state (532) and a determination that theattention state is the attending state, the system transitions (540)through the plurality of sub-content tokens at a predefined rate (e.g.,a constant rate, or a rate proportional to the numerical value of thevariability metric). In either case, regardless of whether thedetermination is that the attention state is the attending state or thenot-attending state, in some implementations, a first sub-content tokenof the plurality of sub-tokens includes (542) visual content displayedon a first region of the display and the plurality of sub-content tokensincludes visual content displayed on a second region of the display thatis larger than the first region. Furthermore, in some implementations,the predefined rate is (544) proportional to a numerical value of thevariability metric, while in some other implementations, the predefinedrate is (546) a constant rate of sub-content tokens per unit time.

In some implementations, the content includes (548) an advertisement. Inaccordance with a determination that the attention state is theattending state and that the locus of attention corresponds to thecontent, the system generates (550) a cost-per-action metric. In someimplementations, the generated cost-per-action metric is generated (552)in accordance with a length of time that attention state is theattending state and a locus of attention corresponds to the content.

For example, in some implementations, a car advertisement is displayedin a respective region of the display. At first, the car advertisementis static, for example, showing an attractive picture of a new sportscar. In this example, the attractive picture of the sports car serves asa “lure,” attempting to attract the subject's attention to therespective region of the display in which the advertisement isdisplayed. Optionally, once the system determines that the trajectory ofattention is leading towards the region of the display, the systemmodifies the advertisement by delivering audio content, for example, ofan engine “revving sound,” and showing the wheels of the sports carstarting to spin (e.g., transitioning through sub-content tokens of thecontent, where the sub-content tokens include video frames of the wheelsstarting to spin). Once the system determines that the locus ofattention is the respective region and that the subject's attentionstate is the attending state (e.g., the user is “hooked”), the systemmodifies the advertisement to show, for example, footage of the carbeing driven by a professional driver on a closed course. In someembodiments, the displayed speed of the car is inversely proportional tothe variability metric (e.g., a lower level of variability generates ahigher displayed car speed as compared to a higher level ofvariability).

FIGS. 6A-6C illustrate a flow diagram of a method 600 for generating acost-per-action metric for advertising, in accordance with someembodiments. The method is, optionally, governed by instructions thatare stored in a computer memory or non-transitory computer readablestorage medium (e.g., memory 212 in FIG. 2) and that are executed by oneor more processors (e.g., processor(s) 202) of one or more computersystems, including, but not limited to, system 100 (FIG. 1) and/orsystem 200 (FIG. 2). The computer readable storage medium may include amagnetic or optical disk storage device, solid state storage devicessuch as Flash memory, or other non-volatile memory device or devices.The computer readable instructions stored on the computer readablestorage medium may include one or more of: source code, assemblylanguage code, object code, or other instruction format that isinterpreted by one or more processors. In various implementations, someoperations in each method may be combined and/or the order of someoperations may be changed from the order shown in the figures. Also, insome implementations, operations shown in separate figures and/ordiscussed in association with separate methods (e.g., method 500, FIG. 5or method 700, FIG. 7) may be combined to form other methods, andoperations shown in the same figure and/or discussed in association withthe same method may be separated into different methods. Moreover, insome implementations, one or more operations in the methods areperformed by modules of system 200 shown in FIG. 2, including, forexample, processor(s) 202, user interface 204, memory 212, networkinterface 210, and/or any sub modules thereof.

In some implementations, method 600 is performed at a system includingone or more processors and memory storing instructions for execution bythe one or more processors (e.g., system 100 or system 200). Method 600includes presenting (602) a subject with an advertisement. In someembodiments, the advertisement includes audio content, visual content(printed text, video, still frame images, etc), tactile content, or acombination thereof. In particular, in some embodiments, theadvertisement includes (604) visual content that is displayed on adisplay (e.g., a display of a smart-phone, tablet, personal computer,laptop computer, smart-television, or the like).

While presenting the advertisement to the subject, the system measures(606) one or more of the subject's eye positions. In some embodiments,measuring the one or more of the subject's eye positions includes (608)measuring the subject's right eye positions and measuring the subject'sleft eye positions. In some embodiments, measuring the subject's eyepositions is accomplished (610) by using one or more video cameras. Inaccordance with these embodiments, FIG. 1 shows a system includingdigital video camera(s) 104 for measuring subject 102's eye positions.

In some embodiments (e.g., when the advertisement includes displayedvisual content), the system continuously determines (612) a locus ofattention on the display using the one or more measured eye positions.

The system generates (614) a variability metric using the one or moremeasured eye positions. In some embodiments, the variability metric isgenerated continuously (e.g., in real-time, at a periodic rate of noless than 5 Hz). In some embodiments, generating the variability metricusing the one or more measured eye positions includes (616) comparingthe measured right eye positions with the measured left eye positions.In such circumstances, the variability metric is a disconjugacy metric.In some embodiments, generating the disconjugacy metric includescalculating a plurality of results of the difference of the position ofthe subject's right eye from the position of the subject's left eye(e.g., by subtracting the position of the subject's right eye from theposition of the subject's left eye). In some embodiments, generating thedisconjugacy metric includes calculating a variability (e.g., adispersion, scatter, or spread) of the plurality of results, where eachresult corresponds to a distinct time. For example, in some embodiments,the disconjugacy metric corresponds (618) to a standard deviation ofdifferences between the subject's right eye position and the subject'sleft eye position over a shifting window of time. In some embodiments,generating the disconjugacy metric includes generating a vertical metricand generating a horizontal metric, where generating a vertical metricincludes measuring the difference between the subject's eyes along avertical axis and generating a horizontal metric includes measuring thedifference between the subject's eyes along a horizontal axis.

In some embodiments, the system compares the measured eye movements witha movement of a focus of the advertisement. For example, theadvertisement may include a video advertisement. In these circumstances,it is expected that a subject who is attending to the videoadvertisement will track whichever actor in the video advertisement isspeaking Thus, in some circumstances, the system will compare themeasured eye movements to the movements of the speaking actors on thescreen. As another example, the advertisement may be a videoadvertisement for a sports car racing around a closed course. In thesecircumstances, it is expected that a subject who is attending to thevideo advertisement will track the sports car. Thus, in somecircumstances, the system will compare the measured eye movements to themovements of the sports car on the screen. The variability metriccorresponds to how accurately and how consistently the subject visuallytracks movement of the focus of advertisement. To this end, in someembodiments, the system measures both of the subject's eye movements.Alternatively, in some embodiments, the system measures the eyemovements of one of the subject's eyes (e.g., the subject's dominateeye).

In some embodiments, the variability metric (regardless of whether it isa disconjugacy metric or not) is generated (620) based on at least oneof: a variability of eye position error metric, a variability of eyevelocity gain metric, an eye position error metric, and a rate or numberof saccades metric.

Further, the system compares (622) the variability metric with apredetermined baseline to determine an attention state of the subject.In some embodiments, the comparison is performed continuously (e.g., inreal-time). In some embodiments, the attention state is (624) one of anabout-to-attend state, an attending state, or a not-attending state. Forexample, in some embodiments, the attending state corresponds to a lowvariability metric and indicates that the subject is fully attending tothe presented advertisement. In some implementations, the subject'sattention state is the about-to-attend state when the variability metricis decreasing—indicating an increasing level of attention—and thevariability metric is greater than the predetermined baseline but withina predetermined threshold of the predetermined baseline. In someimplementations, the attention state is the not-attending state when theattention state is not the attending state. Alternatively, the attentionis state is the not-attending state when the attention state is neitherthe attending state nor the about-to-attend state.

In some embodiments, the predetermined baseline is (626) based on atleast one of: a variability range (e.g., a disconjugacy range)associated with a preselected group of control subjects (sometimescalled a control group baseline), a demographic of the subject (e.g., anage range, a gender, a socio-economic status, etc.), and a variabilitymetric (e.g., a disconjugacy metric) for the subject generated from aprevious test (sometimes called an individual or personal baseline). Foran example, an individual or personal baseline may be obtained by askingthe individual to track a smoothly moving object on the display for apredefined period of time (e.g., a period of less than 1 minute and noless than 5 seconds, such as 5 seconds, 10 seconds or 30 seconds), andusing eye measurements obtained during the predefined period of time togenerate the personal baseline (e.g., the predefined period of timecomprises a personal baseline calibration period).

In some embodiments, the group of control subjects is composed ofpersons sharing a demographic of the subject. For example, the group ofcontrol subjects is composed of persons having a similar age andsocioeconomic status as the subject. In some embodiments, the group ofcontrol subjects is composed of persons having a similar braindevelopment level as the subject. In some embodiments, the group ofcontrol subjects is composed of persons of the same gender as thesubject.

In some embodiments, the system determines (628) a length of time duringwhich the locus of attention corresponds to the advertisement and theattention state is the attending state.

The system then generates (630) the cost-per-action metric in accordancewith the attention state of the subject. In some embodiments, the systemgenerates (632) the cost-per-action metric in accordance with adetermination that the locus of attention corresponds to theadvertisement (e.g., the subject is not only attending but attending tothe advertisement). In some embodiments, the system generates (634) thecost-per-action metric in accordance with the length of time duringwhich the locus of attention corresponds to the advertisement and theattention state is the attending state. In some embodiments, the systemgenerates (636) the cost-per-action metric in accordance with anumerical value of the variability metric (e.g., the cost-per-actionmetric is generated based at least partially on a quality of thesubject's attention level, for example, by generating a largercost-per-action metric when the variability metric is lower, indicatinga greater level of attention). In some embodiments, the system generates(638) the cost-per-action metric in accordance with a change in thenumerical value of the variability metric (e.g., the cost-per-actionmetric is generated based on the advertisements ability to “grab” thesubject's attention, for example, by increasing the cost-per-actionmetric when the variability metric decreases during the advertisement).

FIGS. 7A-7B illustrate a flow diagram of a method 700 for deliveringcontent dynamically based on gaze analytics, in accordance with someembodiments. The method is, optionally, governed by instructions thatare stored in a computer memory or non-transitory computer readablestorage medium (e.g., memory 212 in FIG. 2) and that are executed by oneor more processors (e.g., processor(s) 202) of one or more computersystems, including, but not limited to, system 100 (FIG. 1) and/orsystem 200 (FIG. 2). The computer readable storage medium may include amagnetic or optical disk storage device, solid state storage devicessuch as Flash memory, or other non-volatile memory device or devices.The computer readable instructions stored on the computer readablestorage medium may include one or more of: source code, assemblylanguage code, object code, or other instruction format that isinterpreted by one or more processors. In various implementations, someoperations in each method may be combined and/or the order of someoperations may be changed from the order shown in the figures. Also, insome implementations, operations shown in separate figures and/ordiscussed in association with separate methods (e.g., method 500, FIG. 5or method 600, FIG. 6) may be combined to form other methods, andoperations shown in the same figure and/or discussed in association withthe same method may be separated into different methods. Moreover, insome implementations, one or more operations in the methods areperformed by modules of system 200 shown in FIG. 2, including, forexample, processor(s) 202, user interface 204, memory 212, networkinterface 210, and/or any sub modules thereof.

In some implementations, method 700 is performed at a system includingone or more processors and memory storing instructions for execution bythe one or more processors (e.g., system 100 or system 200). Method 700includes presenting (702) a subject with first content. In someembodiments, the first content includes audio content, visual content(printed text, video, still frame images, etc), tactile content, or acombination thereof. In particular, in some embodiments, the firstcontent includes visual content that is displayed on a display (e.g., adisplay of a smart-phone, tablet, personal computer, laptop computer,smart-television, or the like).

While presenting the first content to the subject, the system measures(704) one or more of the subject's eye positions. In some embodiments,measuring the one or more of the subject's eye positions includes (706)measuring the subject's right eye positions and measuring the subject'sleft eye positions. In some embodiments, measuring the subject's eyepositions is accomplished (708) by using one or more video cameras. Inaccordance with these embodiments, FIG. 1 shows a system includingdigital video camera(s) 104 for measuring subject 102's eye positions.

The system continuously generates (710) a variability metric using theone or more measured eye positions. In some embodiments, generating thevariability metric using the one or more measured eye positions includes(712) compare the measured right eye positions with the measured lefteye positions. In such circumstances, the variability metric is adisconjugacy metric. In some embodiments, generating the disconjugacymetric includes calculating a plurality of results of the difference ofthe position of the subject's right eye from the position of thesubject's left eye (e.g., by subtracting the position of the subject'sright eye from the position of the subject's left eye). In someembodiments, generating the disconjugacy metric includes calculating avariability (e.g., a dispersion, scatter, or spread) of the plurality ofresults, where each result corresponds to a distinct time. For example,in some embodiments, the disconjugacy metric corresponds (714) to astandard deviation of differences between the subject's right eyeposition and the subject's left eye position over a shifting window oftime. In some embodiments, generating the disconjugacy metric includesgenerating a vertical metric and generating a horizontal metric, wheregenerating a vertical metric includes measuring the difference betweenthe subject's eyes along a vertical axis and generating a horizontalmetric includes measuring the difference between the subject's eyesalong a horizontal axis.

In some embodiments, the system compares the measured eye movements witha movement of a focus of the content. For example, the content mayinclude a television program. In these circumstances, it is expectedthat a subject who is attending to the television program will trackwhichever actor in the television program is speaking Thus, in somecircumstances, the system will compare the measured eye movements to themovements of the speaking actors on the screen. The variability metriccorresponds to how accurately and how consistently the subject visuallytracks movement of the focus of content. To this end, in someembodiments, the system measures both of the subject's eye movements.Alternatively, in some embodiments, the system measures the eyemovements of one of the subject's eyes (e.g., the subject's dominateeye).

In some embodiments, the variability metric (regardless of whether it isa disconjugacy metric or not) is generated (716) based on at least oneof: variability of an eye position error metric, variability of an eyevelocity gain metric, an eye position error metric, and a rate or numberof saccades metric.

Further, the system continuously compares (718) the variability metricwith a predetermined baseline to determine an attention state of thesubject. In some embodiments, the attention state is (720) one of anabout-to-attend state, an attending state, or a not attending state. Forexample, in some embodiments, the attending state corresponds to a lowvariability metric and indicates that the subject is fully attending tothe presented content. In some implementations, the subject's attentionstate is the about-to-attend state when the variability metric isdecreasing—indicating an increasing level of attention—and thevariability metric is greater than the predetermined baseline but withina predetermined threshold of the predetermined baseline. In someimplementations, the attention state is the not-attending state when theattention state is not the attending state. Alternatively, the attentionis state is the not-attending state when the attention state is neitherthe attending state nor the about-to-attend state.

In some embodiments, the predetermined baseline is (722) based on atleast one of: a variability range (e.g., a disconjugacy range)associated with a preselected group of control subjects (sometimescalled a control group baseline), a demographic of the subject (e.g., anage range, a gender, a socio-economic status, etc.), and a variabilitymetric (e.g., a disconjugacy metric) for the subject generated from aprevious test (sometimes called an individual or personal baseline). Foran example, an individual or personal baseline may be obtained by askingthe individual to track a smoothly moving object on the display for apredefined period of time (e.g., a period of less than 1 minute and noless than 5 seconds, such as 5 seconds, 10 seconds or 30 seconds), andusing eye measurements obtained during the predefined period of time togenerate a personal baseline (e.g., the predefined period of timecomprises a personal baseline calibration period).

In some embodiments, the group of control subjects is composed ofpersons sharing a demographic of the subject. For example, the group ofcontrol subjects is composed of persons having a similar age andsocioeconomic status as the subject. In some embodiments, the group ofcontrol subjects is composed of persons having a similar braindevelopment level as the subject. In some embodiments, the group ofcontrol subjects is composed of persons of the same gender as thesubject. In some accordance with implementations, an increase in thevariability metric over time may be indicative of fatigue, impairedmental state induced by drugs/alcohol, or waning attention level.

In any event, the system detects (724) a change in the attention stateof the subject. In accordance with the detected change in the attentionstate of the subject, the system presents (726) the subject with secondcontent.

In some circumstances, the detected change in the attention state is(728) a change from the not-attending state to the attending state. Insome such circumstances, the change in the attention state is indicativeof the content “grabbing” the user's attention, which is, in turn,indicative that the subject has an interest in the subject matter of thecontent. Thus, in some circumstances, the second content is thematicallyrelated (730) to the first content. For example, the system may presenta series of advertisements on a variety of different products. During arespective advertisement, if the system detects a change from thenot-attending state to the attending state, in some embodiments thesystem will present a subsequent advertisement that is thematicallyrelated to respective advertisement. For example, the respectiveadvertisement and the subsequent advertisement can be for similarproducts. Alternatively, or in addition, the respective advertisementand the subsequent advertisement can be related in that they aretargeted to a common demographic (e.g., an advertisement for music thattargets a particular demographic may be followed by an advertisement forclothes that targets the same demographic).

In some circumstances, the detected change in the attention state is(732) a change from the attending state to the not-attending state. Insome such circumstances, the change in the attention state is indicativeof the content losing the user's attention, which is, in turn,indicative that the subject has little if any interest in the subjectmatter of the content. Thus, in some circumstances, the second contentis thematically distinct (734) from the first content.

In some circumstances, the second content is provided soon after thefirst content (e.g., the second content interrupts the first content, isprovided concurrently, or follows immediately after). In someembodiments, the detected change in the attention state is used toupdate a profile of the subject, which may be stored on the system or aserver (e.g., an advertisement server). The profile of the subject is,in some embodiments, used for targeted content delivery (e.g., targetedadvertising). In some circumstances, the second content is provided asubstantial amount of time after the first content (e.g., an hour later,a day later, a week later, etc.) in accordance with the updated profileof the subject (and hence, in accordance with the detected change in thesubject's attention state while the first content is being provided).

The foregoing description, for purpose of explanation, has beendescribed with reference to specific embodiments. However, theillustrative discussions above are not intended to be exhaustive or tolimit the invention to the precise forms disclosed. Many modificationsand variations are possible in view of the above teachings. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical applications, to therebyenable others skilled in the art to best utilize the invention andvarious embodiments with various modifications as are suited to theparticular use contemplated.

It will be understood that, although the terms “first,” “second,” etc.may be used herein to describe various elements, these elements shouldnot be limited by these terms. These terms are only used to distinguishone element from another. For example, a first sound detector could betermed a second sound detector, and, similarly, a second sound detectorcould be termed a first sound detector, without changing the meaning ofthe description, so long as all occurrences of the “first sounddetector” are renamed consistently and all occurrences of the “secondsound detector” are renamed consistently. The first sound detector andthe second sound detector are both sound detectors, but they are not thesame sound detector.

The terminology used herein is for the purpose of describing particularimplementations only and is not intended to be limiting of the claims.As used in the description of the implementations and the appendedclaims, the singular forms “a”, “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “comprises” and/or “comprising,” when used inthis specification, specify the presence of stated features, integers,steps, operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

As used herein, the term “if” may be construed to mean “when” or “upon”or “in response to determining” or “in accordance with a determination”or “in response to detecting,” that a stated condition precedent istrue, depending on the context. Similarly, the phrase “if it isdetermined [that a stated condition precedent is true]” or “if [a statedcondition precedent is true]” or “when [a stated condition precedent istrue]” may be construed to mean “upon determining” or “upon adetermination that” or “in response to determining” or “in accordancewith a determination” or “upon detecting” or “in response to detecting”that the stated condition precedent is true, depending on the context.

What is claimed is:
 1. A method of presenting content to a subject basedon eye position measurements, comprising: presenting the subject withcontent; while presenting the content to the subject, measuring one ormore of the subject's eye positions; continuously performing theoperations of: generating a variability metric using the one or moremeasured eye positions; comparing the variability metric with apredetermined baseline to determine an attention state of the subject;and upon detection of a change in the attention state, modifying thepresentation of the content.
 2. The method of claim 1, wherein theattention state is one of an about-to-attend state, an attending state,or a not attending state.
 3. The method of claim 1, wherein the contentincludes visual content displayed on a display; and the method furtherincludes: continuously determining a locus of attention on the displayusing the one or more measured eye positions; and modifying thepresentation of the content in accordance with the continuouslygenerated locus of attention.
 4. The method of claim 3, furtherincluding: using the continuously determined locus of attention,determining a trajectory of the locus of attention; wherein thepresentation of the content is modified in accordance with thetrajectory of the locus of attention.
 5. The method of claim 1, wherein:the content includes a plurality of sub-content tokens; and the methodfurther includes: in accordance with a determination that the attentionstate is the not-attending state, statically presenting a firstsub-content token; and in accordance with a determination that theattention state is the attending state, transitioning through theplurality of sub-content tokens at a predefined rate.
 6. The method ofclaim 5, wherein: the first sub-content token includes visual contentdisplayed on a first region of the display; and the plurality ofsub-content tokens includes visual content displayed on a second regionof the display that is larger than the first region.
 7. The method ofclaim 5, wherein: the predefined rate is proportional to a numericalvalue of the variability metric.
 8. The method of claim 5, wherein: thepredefined rate is a constant rate of sub-content tokens per unit time.9. The method of claim 3, wherein the content includes an advertisement;and the method further includes, in accordance with a determination thatthe attention state is the attending state and that the locus ofattention corresponds to the content, generating a cost-per-actionmetric.
 10. The method of claim 9, wherein the generated cost-per-actionmetric is generated in accordance with a length of time that attentionstate is the attending state and the locus of attention corresponds tothe content.
 11. The method of claim 1, wherein: measuring the one ormore of the subject's eye positions includes: measuring the subject'sright eye positions; measuring the subject's left eye positions; andgenerating the variability metric using the one or more measured eyepositions includes comparing the measured right eye positions with themeasured left eye positions, wherein the variability metric is adisconjugacy metric.
 12. The method of claim 11, wherein thedisconjugacy metric corresponds to a standard deviation of differencesbetween the subject's right eye position and the subject's left eyeposition over a predefined period of time.
 13. The method of claim 1,wherein the variability metric is generated based on at least one of: avariability of eye position error metric; a variability of eye velocitygain metric; an eye position error metric; and a rate or number ofsaccades metric.
 14. The method of claim 1, wherein the predeterminedbaseline is based on at least one of: a variability range associatedwith a preselected group of control subjects; a demographic of thesubject; and a variability metric for the subject generated from aprevious test.
 15. The method of claim 1, wherein measuring thesubject's eye positions is accomplished using one or more video cameras.16. The method of claim 1, wherein the variability metric iscontinuously generated using one or more eye measurements measuredwithin a continuously shifting window of time.
 17. A system forpresenting content to a subject based on eye position measurements,comprising: one or more sensors for measuring one or more of thesubject's eye positions; one or more processors; memory; and one or moreprograms stored in the memory, the one or more programs comprisinginstructions that when executed by the one or more processors cause thesystem to: present the subject with content; while presenting the firstcontent to the subject, measure one or more of the subject's eyepositions; continuously perform the operations of: generating avariability metric using the one or more measured eye positions;comparing the variability metric with a predetermined baseline todetermine an attention state of the subject; and upon detection of achange in the attention state, modify the presentation of the content.18. A non-transitory computer readable storage medium storing one ormore programs, the one or more programs comprising instructions, whichwhen executed by an electronic system with one or more sensors formeasuring eye position, cause the system to: present the subject withcontent; while presenting the first content to the subject, measure oneor more of the subject's eye positions; continuously perform theoperations of: generating a variability metric using the one or moremeasured eye positions; comparing the variability metric with apredetermined baseline to determine an attention state of the subject;and upon detection of a change in the attention state, modify thepresentation of the content.